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start_session_continue_notebook

Fork an existing notebook with a fresh kernel for new sessions without re-executing previous cells. This tool enables continuation of notebook work in JupyterLab sessions on GPU-accelerated compute nodes.

Instructions

Continue a notebook: fork it with fresh kernel (no re-execution).

Args: experiment_name: Name for this session. notebook_path: Path to existing notebook to fork.

Returns: Dict with session_id, notebook_path (forked), job_id, hostname.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
experiment_nameYes
notebook_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors: forking a notebook, using a fresh kernel, and not re-executing code. However, it lacks details about permissions, rate limits, session lifecycle, or error conditions. For a session management tool with zero annotation coverage, this is a moderate gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly structured and concise: a one-sentence purpose statement followed by clearly labeled Args and Returns sections. Every sentence earns its place with no wasted words, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (session management with forking), no annotations, and an output schema that documents return values, the description is reasonably complete. It covers purpose, parameters, and return structure, though additional behavioral context (like auth or error handling) would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides clear semantic explanations for both parameters: 'experiment_name: Name for this session' and 'notebook_path: Path to existing notebook to fork'. This adds meaningful context beyond the bare schema types, though it doesn't cover format details or constraints.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('continue a notebook', 'fork it with fresh kernel') and distinguishes it from sibling tools like 'start_new_session' and 'start_session_resume_notebook' by specifying 'no re-execution'. It precisely identifies the resource (notebook) and action (forking with fresh kernel).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides usage guidance by stating 'continue a notebook: fork it with fresh kernel (no re-execution)', which clearly differentiates it from alternatives like 'start_session_resume_notebook' (which likely resumes with execution) and 'start_new_session' (which doesn't fork an existing notebook). It tells the agent exactly when to use this tool versus its siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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